Computational and omics-driven drug discovery


With the aim of developing novel therapeutics, we integrate different wet lab and computational methodologies that can lead us to discover new protein targets and design candidate molecules to test for activity and possibly develop into new drugs. Proteomic analysis, in complement with other –omics approaches and with in vitro molecular biology and cellular studies, allows us to characterize proteins and peptides, gain structural information, and identify post-translation modifications and functional interactions. This leads us to identify and sequence protein targets and to define biomarkers. The computational drug discovery process is enhanced by using generative deep learning models that expand the chemical space of possible hits on the target. Our approach strongly relies on in silico virtual screening and molecular docking on the target all-atom structural models subjected to Molecular Dynamics. The candidate hits can then enter the experimental pipeline for in vitro studies. Prediction of biological activity and pharmacokinetic parameters can take advantage of artificial intelligence tools.



In silico design of bioactive molecules

  • Host-guest interaction prediction (molecular docking and free energy calculation techniques)
  • Receptor-ligand complex stability calculations (molecular dynamics simulations and free energy calculations)
  • Ligand-based virtual screening (pharmacophore- and QSAR-based screening)
  • Artificial intelligence-based generation of focused virtual libraries by deep learning approaches
  • Structure-based hit and lead optimization (FEP, R-group enumeration)
  • ADME-Tox prediction of small molecules (QSAR/QSPR- and decision tree-based approaches)

Protein and protein complexes modelling and design

  • Protein modelling and design (homology modelling, fold recognition)
  • Prediction of mutation effects on protein structure and function (molecular dynamics simulations, free energy calculation methods, computational alanine scanning)
  • Membrane protein modelling, simulations and design function (molecular dynamics simulations, free energy calculation methods)
  • In silico design and optimization of bioactive peptides (molecular dynamics simulations, molecular docking, free energy calculation methods)
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